Zhou Min-Gang, Cao Xiao-Yu, Lu Yu-Shuo, Wang Yang, Bao Yu, Jia Zhao-Ying, Fu Yao, Yin Hua-Lei, Chen Zeng-Bing
National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
MatricTime Digital Technology Co. Ltd., Nanjing 211899, China.
Research (Wash D C). 2022 Apr 30;2022:9798679. doi: 10.34133/2022/9798679. eCollection 2022.
An increasing number of communication and computational schemes with quantum advantages have recently been proposed, which implies that quantum technology has fertile application prospects. However, demonstrating these schemes experimentally continues to be a central challenge because of the difficulty in preparing high-dimensional states or highly entangled states. In this study, we introduce and analyze a quantum coupon collector protocol by employing coherent states and simple linear optical elements, which was successfully demonstrated using realistic experimental equipment. We showed that our protocol can significantly reduce the number of samples needed to learn a specific set compared with the classical limit of the coupon collector problem. We also discuss the potential values and expansions of the quantum coupon collector by constructing a quantum blind box game. The information transmitted by the proposed game also broke the classical limit. These results strongly prove the advantages of quantum mechanics in machine learning and communication complexity.
近年来，越来越多具有量子优势的通信和计算方案被提出，这意味着量子技术有着广阔的应用前景。然而，由于难以制备高维态或高度纠缠态，通过实验证明这些方案仍然是一个核心挑战。在本研究中，我们引入并分析了一种利用相干态和简单线性光学元件的量子优惠券收集协议，该协议已使用实际实验设备成功演示。我们表明，与优惠券收集问题的经典极限相比，我们的协议可以显著减少学习特定集合所需的样本数量。我们还通过构建量子盲盒游戏讨论了量子优惠券收集器的潜在价值和扩展。所提出的游戏传输的信息也突破了经典极限。这些结果有力地证明了量子力学在机器学习和通信复杂性方面的优势。
